
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn.functional as F


# __all__ = ['Res2Net', 'res2net50']



class Bottle2neck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, baseWidth=26, scale=4, stype='normal', hasSE=False):
        """ Constructor
        Args:
            inplanes: input channel dimensionality          #输入通道维度
            planes: output channel dimensionality           #输出通道维度
            stride: conv stride. Replaces pooling layer.    #conv的步长
            downsample: None when stride = 1                #下采样
            baseWidth: basic width of conv3x3               #基本宽度
            scale: number of scale.                         #分组的数目
            type: 'normal': normal set. 'stage': first block of a new stage.
        """
        super(Bottle2neck, self).__init__()

        # 1x1卷积,先降维到 width * scale
        # 主要是为了减少网络变深以后的参数量
        width = int(math.floor(planes * (baseWidth / 64.0)))
        self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width * scale)

        # scale表示用到的3x3卷积层个数
        # nums=scale-1,便于访问最后一个块的下标
        if scale == 1:
            self.nums = 1
        else:
            self.nums = scale - 1

        # stype设置为stage时，在分组卷积中直接输出的组后添加一个平均池化层，使它与其他经过下采样卷积的组对应
        # stage类型出现在层交界处的第一个Bottleneck块处，它改变输出维度以适应新的层的维度
        if stype == 'stage':
            self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)

        # scale-1个3x3卷积
        convs = []
        bns = []
        for i in range(self.nums):
            convs.append(nn.Conv2d(width, width, groups=8, kernel_size=3, stride=stride, padding=1, bias=False))
            bns.append(nn.BatchNorm2d(width))
        self.convs = nn.ModuleList(convs)
        self.bns = nn.ModuleList(bns)

        # 1x1卷积，恢复维度
        self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)

        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stype = stype
        self.scale = scale
        self.width = width

    def forward(self, x):
        residual = x

        # 1x1卷积,降维
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        # 多个3x3卷积层
        # 将输出分为scale份，每份的宽度为width
        spx = torch.split(out, self.width, 1)
        # 这里与论文图示有点区别
        # 前scale-1个块进行逐层卷积，最后一个块不卷积直接输出
        for i in range(self.nums):
            if i == 0 or self.stype == 'stage':
                # stage时直接输出
                sp = spx[i]
            else:
                # 每次卷积前，将输入与前一次卷积结果相加
                sp = sp + spx[i]
            sp = self.convs[i](sp)
            sp = self.relu(self.bns[i](sp))
            if i == 0:
                out = sp
            else:
                out = torch.cat((out, sp), 1)
        # 处理最后一个块，直接输出
        if self.scale != 1 and self.stype == 'normal':
            out = torch.cat((out, spx[self.nums]), 1)
        elif self.scale != 1 and self.stype == 'stage':
            out = torch.cat((out, self.pool(spx[self.nums])), 1)

        out = self.conv3(out)
        out = self.bn3(out)

        # 块的stride不为1或维度有大小变化（stage），shortcut需要下采样才能和卷积结果大小一致
        if self.downsample is not None:
            residual = self.downsample(x)

        # 输出和残差相加后relu
        out += residual
        out = self.relu(out)

        return out


class Res2Net(nn.Module):

    def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):
        self.inplanes = 64
        super(Res2Net, self).__init__()
        self.baseWidth = baseWidth
        self.scale = scale

        # 7x7卷积，步长2
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)

        # 3x3最大池化，步长2
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # 四个layer，其中的bottleneck块数量为[3,4,6,3]
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        # 平均池化
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        # 初始化卷积和bn
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        # shortcut需要下采样的情况: stride不为1或输入特征图的通道数与输出特征图的通道数不同
        # 下采样：1x1卷积层+BatchNorm层
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,  kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        # 每个layer的第一个block是stage类型，带有stride参数
        # stride=2时，输出的特征图尺寸减半，维度加倍，因此中间过程尺寸可能不匹配
        # satge参数让分组卷积不使用相邻卷积结果（尺寸不匹配），同时让shortcut下采样
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample=downsample, stype='stage', baseWidth=self.baseWidth, scale=self.scale))

        # stage类型的block会将输出通道数转变为标准的planes * block.expansion，以便后面的block使用
        # 剩下的block数量根据给出的参数生成
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, baseWidth=self.baseWidth, scale=self.scale))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def res2net50_g(pretrained=False, **kwargs):
    """Constructs a Res2Net-50 model.
    Res2Net-50 refers to the Res2Net-50_26w_4s.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=32, scale=4, **kwargs)
    return model





if __name__ == '__main__':
    images = torch.rand(1, 3, 224, 224).cuda(0)
    # model = res2net101_26w_4s(pretrained=True)
    # model = model.cuda(0)
    # print(model(images).size())
